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When to use generalized estimating equations vs. mixed effects models?

I have a dataset from collected by cluster randomized sampling, I did a logistic regression on this dataset assuming that the dataset is collected by simple random sampling. Now I realize that it may not be appropriate to use simple logistic regression and I am thinking of re-analyzing it.

I am come across two terms: "Multi-level modelling" and "Generalized Estimating Equation". I am using the LEMMA online self-learning resources to learn Multi-level modelling and I haven't read anything about Generalized Estimating Equation yet.

I wonder if two are completely different things? Looks like both can handle cluster randomzied sampled data, but I don't have the details. Can anyone tell me a bit more about the two methods?

Please feel free to comment if further information is needed. Thanks.

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lokheart
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    You can start by taking a look at this links: http://stats.stackexchange.com/questions/16390/when-to-use-generalized-estimating-equations-vs-mixed-effects-models http://stats.stackexchange.com/questions/32419/difference-between-generalized-linear-models-generalized-linear-mixed-models-i – boscovich Jul 23 '12 at 06:57
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    Or this: [What is the difference between generalized estimating equations and GLMM?](http://stats.stackexchange.com/q/17331/930) – chl Jul 23 '12 at 07:59

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